Data Architecture A Primer For The Data Scientist

Author: W.H. Inmon
Publisher: Morgan Kaufmann
ISBN: 0128020911
Size: 25.16 MB
Format: PDF
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Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or architecture it can’t be used to its full potential. Data Architecture a Primer for the Data Scientist addresses the larger architectural picture of how Big Data fits with the existing information infrastructure, an essential topic for the data scientist. Drawing upon years of practical experience and using numerous examples and an easy to understand framework. W.H. Inmon, and Daniel Linstedt define the importance of data architecture and how it can be used effectively to harness big data within existing systems. You’ll be able to: Turn textual information into a form that can be analyzed by standard tools. Make the connection between analytics and Big Data Understand how Big Data fits within an existing systems environment Conduct analytics on repetitive and non-repetitive data Discusses the value in Big Data that is often overlooked, non-repetitive data, and why there is significant business value in using it Shows how to turn textual information into a form that can be analyzed by standard tools. Explains how Big Data fits within an existing systems environment Presents new opportunities that are afforded by the advent of Big Data Demystifies the murky waters of repetitive and non-repetitive data in Big Data

Building A Scalable Data Warehouse With Data Vault 2 0

Author: Dan Linstedt
Publisher: Morgan Kaufmann
ISBN: 0128026480
Size: 53.63 MB
Format: PDF, Mobi
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The Data Vault was invented by Dan Linstedt at the U.S. Department of Defense, and the standard has been successfully applied to data warehousing projects at organizations of different sizes, from small to large-size corporations. Due to its simplified design, which is adapted from nature, the Data Vault 2.0 standard helps prevent typical data warehousing failures. "Building a Scalable Data Warehouse" covers everything one needs to know to create a scalable data warehouse end to end, including a presentation of the Data Vault modeling technique, which provides the foundations to create a technical data warehouse layer. The book discusses how to build the data warehouse incrementally using the agile Data Vault 2.0 methodology. In addition, readers will learn how to create the input layer (the stage layer) and the presentation layer (data mart) of the Data Vault 2.0 architecture including implementation best practices. Drawing upon years of practical experience and using numerous examples and an easy to understand framework, Dan Linstedt and Michael Olschimke discuss: How to load each layer using SQL Server Integration Services (SSIS), including automation of the Data Vault loading processes. Important data warehouse technologies and practices. Data Quality Services (DQS) and Master Data Services (MDS) in the context of the Data Vault architecture. Provides a complete introduction to data warehousing, applications, and the business context so readers can get-up and running fast Explains theoretical concepts and provides hands-on instruction on how to build and implement a data warehouse Demystifies data vault modeling with beginning, intermediate, and advanced techniques Discusses the advantages of the data vault approach over other techniques, also including the latest updates to Data Vault 2.0 and multiple improvements to Data Vault 1.0

Super Charge Your Data Warehouse

Author: Dan Linstedt
Publisher: CreateSpace
ISBN: 9781463778682
Size: 63.13 MB
Format: PDF, Mobi
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Do You Know If Your Data Warehouse Flexible, Scalable, Secure and Will It Stand The Test Of Time And Avoid Being Part Of The Dreaded "Life Cycle"? The Data Vault took the Data Warehouse world by storm when it was released in 2001. Some of the world's largest and most complex data warehouse situations understood the value it gave especially with the capabilities of unlimited scaling, flexibility and security. Here is what industry leaders say about the Data Vault "The Data Vault is the optimal choice for modeling the EDW in the DW 2.0 framework" - Bill Inmon, The Father of Data Warehousing "The Data Vault is foundationally strong and an exceptionally scalable architecture" - Stephen Brobst, CTO, Teradata "The Data Vault should be considered as a potential standard for RDBMS-based analytic data management by organizations looking to achieve a high degree of flexibility, performance and openness" - Doug Laney, Deloitte Analytics Institute "I applaud Dan's contribution to the body of Business Intelligence and Data Warehousing knowledge and recommend this book be read by both data professionals and end users" - Howard Dresner, From the Foreword - Speaker, Author, Leading Research Analyst and Advisor You have in your hands the work, experience and testing of 2 decades of building data warehouses. The Data Vault model and methodology has proven itself in hundreds (perhaps thousands) of solutions in Insurance, Crime-Fighting, Defense, Retail, Finance, Banking, Power, Energy, Education, High-Tech and many more. Learn the techniques and implement them and learn how to build your Data Warehouse faster than you have ever done before while designing it to grow and scale no matter what you throw at it. Ready to "Super Charge Your Data Warehouse"?

Practical Data Science

Author: Andreas François Vermeulen
Publisher: Apress
ISBN: 148423054X
Size: 13.15 MB
Format: PDF, ePub
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Learn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets. The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions. What You'll Learn Become fluent in the essential concepts and terminology of data science and data engineering Build and use a technology stack that meets industry criteria Master the methods for retrieving actionable business knowledge Coordinate the handling of polyglot data types in a data lake for repeatable results Who This Book Is For Data scientists and data engineers who are required to convert data from a data lake into actionable knowledge for their business, and students who aspire to be data scientists and data engineers

Modeling The Agile Data Warehouse With Data Vault

Author: Hans Hultgren
Publisher:
ISBN: 9780615723082
Size: 59.14 MB
Format: PDF, ePub
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Data Modeling for Agile Data Warehouse using Data Vault Modeling Approach. Includes Enterprise Data Warehouse Architecture. This is a complete guide to the data vault data modeling approach. The book also includes business and program considerations for the agile data warehousing and business intelligence program. There are over 200 diagrams and figures concerning modeling, core business concepts, architecture, business alignment, semantics, and modeling comparisons with 3NF and Dimensional modeling.

Dw 2 0 The Architecture For The Next Generation Of Data Warehousing

Author: W.H. Inmon
Publisher: Elsevier
ISBN: 9780080558332
Size: 72.70 MB
Format: PDF, Docs
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DW 2.0: The Architecture for the Next Generation of Data Warehousing is the first book on the new generation of data warehouse architecture, DW 2.0, by the father of the data warehouse. The book describes the future of data warehousing that is technologically possible today, at both an architectural level and technology level. The perspective of the book is from the top down: looking at the overall architecture and then delving into the issues underlying the components. This allows people who are building or using a data warehouse to see what lies ahead and determine what new technology to buy, how to plan extensions to the data warehouse, what can be salvaged from the current system, and how to justify the expense at the most practical level. This book gives experienced data warehouse professionals everything they need in order to implement the new generation DW 2.0. It is designed for professionals in the IT organization, including data architects, DBAs, systems design and development professionals, as well as data warehouse and knowledge management professionals. * First book on the new generation of data warehouse architecture, DW 2.0. * Written by the "father of the data warehouse", Bill Inmon, a columnist and newsletter editor of The Bill Inmon Channel on the Business Intelligence Network. * Long overdue comprehensive coverage of the implementation of technology and tools that enable the new generation of the DW: metadata, temporal data, ETL, unstructured data, and data quality control.

Client Side Data Storage

Author: Raymond Camden
Publisher: "O'Reilly Media, Inc."
ISBN: 1491935081
Size: 76.23 MB
Format: PDF, Kindle
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One of the most useful features of today’s modern browsers is the ability to store data right on the user’s computer or mobile device. Even as more people move toward the cloud, client-side storage can still save web developers a lot of time and money, if you do it right. This hands-on guide demonstrates several storage APIs in action. You’ll learn how and when to use them, their plusses and minuses, and steps for implementing one or more of them in your application. Ideal for experienced web developers familiar with JavaScript, this book also introduces several open source libraries that make storage APIs easier to work with. Learn how different browsers support each client-side storage API Work with web (aka local) storage for simple things like lists or preferences Use IndexedDB to store nearly anything you want on the user’s browser Learn how support web apps that still use the discontinued Web SQL Database API Explore Lockr, Dexie, and localForage, three libraries that simplify the use of storage APIs Build a simple working application that makes use of several storage techniques

The Data Model Toolkit

Author: Dave Knifton
Publisher: Paragon Publishing
ISBN: 1782224734
Size: 55.54 MB
Format: PDF, Kindle
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Adopting the latest technological and data related innovations has caused many organisations to realise they don’t have a firm grasp on their basic operational data. This is a problem that Logical Data Models are uniquely qualified to help them solve. The realisation of the need to define a Logical Data Model may be driven by any number of reasons including; trying to link Big Data Analytics to operational data, plunging into Digital Marketing, choosing the best SaaS solution, carrying out a core Data Migration, developing a Data Warehouse, enhancing Data Governance processes, or even just trying to get everyone to agree on their Product specifications! This book will provide you with the skills required to start to answer these and many similar types of questions. It is not written with a focus on IT development, so you don’t need a technical background to get the most from it. But for any professional working in an organisation’s data landscape, this book will provide the skills they need to define high quality and beneficial data models quickly and easily. It does this using a wealth of practical examples, tips and techniques, as well as providing checklists and templates. It is structured into three parts: The Foundations: What are the solid foundations necessary for building effective data models? The Tools: What Tools are required to enable you to specify clear, precise and accurate data model definitions? The Deliverables: What processes will you need to successfully define the models, what will they deliver, and how can we make them beneficial to the organisation? “In this data-rich era, it is even more critical for organisations to answer the question of what their data means and the value it can bring. Those who can, will gain a competitive advantage through their use of data to streamline their operations and energise their strategies. Core to revealing this meaning, is the data model that is now, more than ever, the lynchpin of success. The Data Model Toolkit provides the essential knowledge and skills that will ensure this success.” – Reem Zahran, Global IT Platform Director, TNS “We work with many enterprise customers to help them transform their technology and it always starts with data. The key is a clear definition of their data quality, completeness and governance. This book shows you step by step how to define and use Data Models as powerful tools to define an organisation’s data and maximise its business benefit.” – John Casserly, CEO, Xceed Group